Skip to main content

Implementation of K-Mean Cluster

# Step 1: Import Libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_wine

# Step 2: Load the Wine Dataset
wine = load_wine()
data = pd.DataFrame(wine.data, columns=wine.feature_names)
# Display the first few rows of the dataset
print("First few rows of the dataset:")
print(data.head())

# Step 3: Data Preprocessing
# Standardize the features
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data)
print("\nStandardized Data (first few rows):")
print(scaled_data[:5])

# Step 4: Apply K-means Clustering
# Setting the number of clusters directly (e.g., k = 3)
k = 3
kmeans = KMeans(n_clusters=k, random_state=42)
clusters = kmeans.fit_predict(scaled_data)
# Add cluster labels to the original data
data['Cluster'] = clusters
# Print Centroid Values
centroids = kmeans.cluster_centers_
print("\nCentroid values (scaled):")
print(centroids)

# Step 5: Visualize the Clusters
# We will plot only the first two features for simplicity
plt.figure(figsize=(10, 6))
plt.scatter(scaled_data[:, 0], scaled_data[:, 1], c=clusters, cmap='viridis')
plt.scatter(centroids[:, 0], centroids[:, 1], s=300, c='red', marker='X', label='Centroids')
plt.title('K-means Clustering on Wine Dataset')
plt.xlabel('Feature 1 (alcohol)')
plt.ylabel('Feature 2 (malic_acid)')
plt.colorbar(label='Cluster')
plt.legend()
plt.show()

# Step 6: Analyze Results
# Print mean values of each feature per cluster
cluster_analysis = data.groupby('Cluster').mean()
print("\nMean values of each feature per cluster:")
print(cluster_analysis)

Output:



Mean values of each feature per cluster: alcohol malic_acid ash alcalinity_of_ash magnesium \ Cluster 0 12.250923 1.897385 2.231231 20.063077 92.738462 1 13.134118 3.307255 2.417647 21.241176 98.666667 2 13.676774 1.997903 2.466290 17.462903 107.967742 total_phenols flavanoids nonflavanoid_phenols proanthocyanins \ Cluster 0 2.247692 2.050000 0.357692 1.624154 1 1.683922 0.818824 0.451961 1.145882 2 2.847581 3.003226 0.292097 1.922097 color_intensity hue od280/od315_of_diluted_wines proline Cluster 0 2.973077 1.062708 2.803385 510.169231 1 7.234706 0.691961 1.696667 619.058824 2 5.453548 1.065484 3.163387 1100.225806


Comments

Post a Comment

Popular posts from this blog

ML Lab Questions

1. Using matplotlib and seaborn to perform data visualization on the standard dataset a. Perform the preprocessing b. Print the no of rows and columns c. Plot box plot d. Heat map e. Scatter plot f. Bubble chart g. Area chart 2. Build a Linear Regression model using Gradient Descent methods in Python for a wine data set 3. Build a Linear Regression model using an ordinary least-squared model in Python for a wine data set  4. Implement quadratic Regression for the wine dataset 5. Implement Logistic Regression for the wine data set 6. Implement classification using SVM for Iris Dataset 7. Implement Decision-tree learning for the Tip Dataset 8. Implement Bagging using Random Forests  9.  Implement K-means Clustering    10.  Implement DBSCAN clustering  11.  Implement the Gaussian Mixture Model  12. Solve the curse of Dimensionality by implementing the PCA algorithm on a high-dimensional 13. Comparison of Classification algorithms  14. Compa...
Post a Comment
Read more

DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular density-based clustering algorithm that groups data points based on their density in feature space. It’s beneficial for datasets with clusters of varying shapes, sizes, and densities, and can identify noise or outliers. Step 1: Initialize Parameters Define two important parameters: Epsilon (ε) : The maximum distance between two points for them to be considered neighbors. Minimum Points (minPts) : The minimum number of points required in an ε-radius neighborhood for a point to be considered a core point. Step 2: Label Each Point as Core, Border, or Noise For each data point P P P in the dataset: Find all points within the ε radius of P P P (the ε-neighborhood of P P P ). Core Point : If P P P has at least minPts points within its ε-neighborhood, it’s marked as a core point. Border Point : If P P P has fewer than minPts points in its ε-neighborhood but is within the ε-neighborhood of a core point, it’...
Post a Comment
Read more

Gaussian Mixture Model

A Gaussian Mixture Model (GMM) is a probabilistic model used for clustering and density estimation. It assumes that data is generated from a mixture of several Gaussian distributions, each representing a cluster within the dataset. Unlike K-means, which assigns data points to the nearest cluster centroid deterministically, GMM considers each data point as belonging to each cluster with a certain probability, allowing for soft clustering. GMM is ideal when: Clusters have elliptical shapes or different spreads : GMM captures varying shapes and densities, unlike K-means, which assumes clusters are spherical. Soft clustering is preferred : If you want to know the probability of a data point belonging to each cluster (not a hard assignment). Data has overlapping clusters : GMM allows a point to belong partially to multiple clusters, which is helpful when clusters have significant overlap. Applications of GMM Image Segmentation : Used to segment images into regions, where each region can be...
Post a Comment
Read more